As Web 2.0 and enterprise-cloud applications have proliferated, data collection processes increasingly require the ability to efficiently and effectively handle web-scale datasets. Such processes include, but are not limited to, chemical and mechanical manufacturing optimizations or economic regret formulations, where the corresponding chemical or mechanical waste or economic loss is minimized. Of particular interest is the analysis of “dyadic data,” which concerns discovering and capturing interactions between two entities. For example, certain applications involve topic detection and keyword search, where the corresponding entities are documents and terms. Other examples concern news personalization with user and story entities, and recommendation systems with user and item entities. In large applications, these problems often involve matrices with millions of rows (e.g., distinct customers) and millions of columns (e.g., distinct items), ultimately resulting in billions of populated cells (e.g., transactions between customers and items). In these data collection applications, the corresponding optimization problem is to compute row and column profiles, such that the loss between the “predicted” cells (from the corresponding row and column profiles) and the actual cells is minimized.